RNA impacts nearly every aspect of gene expression and many human diseases are caused by or result in mistakes in RNA metabolism, e.g. mutations in pre-mRNAs lead to splicing defects or degradation of mRNAs by trigger nonsense-mediated mRNA decay. It has been shown that in addition to RNA's fundamental roles in information transfer from DNA to protein, RNA molecules play crucial roles in gene regulation as their stability or rate of protein synthesis is regulated by mRNA binding proteins or ribonucleoprotein complexes (RNPs), e.g. microRNA-containing RNPs.
Given this widespread clinical utility, there is a significant need to develop and optimize RNA detection, quantitation, and visualization assays for diagnostic purposes.
For example, recent studies link micro RNAs (“miRNAs”) with diseases such as cancer and neurological disorders. To date, miRNA profiling has been used to classify cancers of known and unknown primary origin, determine prognosis and disease progression, predict chemoresistance, monitor therapy, and screen for disease.
miRNAs have specific expression and function in specialized cell types, emphasizing the need to define cell-type-specific miRNA expression patterns. The most common method for visualizing gene expression in specific cell types is in situ hybridization (ISH).
However, conventional ISH methods permit the release and diffusion of small nucleic acids, such as miRNA, from tissue. As a consequence, current ISH are not reliable for measuring small nucleic acids such as miRNA.
To establish miRNA-based diagnostics, it is essential to define a clinical need, reliably extract small RNAs from clinical materials, detect and quantitate miRNA expression differences between samples, and establish tractable molecular tests for clinical laboratory use.
Developing quantitative RNA ISH methods to detect cell-type-specific gene expression signatures in archived clinical materials would be a major advance in molecular diagnostics. Because RNA remains intact in archived samples (though partially hydrolyzed into shorter segments) and next-generation RNA sequencing is being used to resolve gene expression variation at the cellular level in tissue sections, RNA visualization methods are urgently needed.
Therefore, there remains a need for improved methods to fix, retain, quantify, visualize and detect RNA in a biological sample.